I'd say that the way to approach this problem greatly depends on what you want to do and why you need the 3D structure.
What grade of precision do you need and what do you need to measure?
Molecules move all the time unless crystallized. Do you need the crystal structure or do you need a representative conformation that is often assumed in a solvent at room temperature?
If you just need to represent the molecules in 3D in a conformation that "makes sense" for small molecules, a force field based method will generally work and produce conformations that work well enough; otherwise you can try some semiempirical methods, those are quite fast and cheap as well.
DFT will allow you to predict with better precision the conformation such a molecule would assume in vacuum at 0K (unless you go through several passages to try and model something else).
A third option could be using experimentally measured crystal structures, though I don't know how comfortable it is to go finding and cleaning up 10,000 of them.
I would not use databases containing computed structures such as DrugBank as you'd have no control over the process nor the possibility to check the quality of the results.
You may obtain quite different conformations using the different methodologies. Your model may work on some molecules because they have a particular conformation and fail on others that would normally have a similar one, however also have another different one that comes out slightly favorable in the calculations. 3D QSAR is particularly complex because of this and due to the difficulties in encoding molecular information in a comparable way. In a three dimensional object it's much more difficult to look for similarities and dissimilarities.
Regarding DFT computation time, that greatly depends on the sampling method you use to search the conformational space. Performing a steepest descent/BFGS may give you results in a couple minutes when we're talking about small molecules. However that won't work when searching for a global minimum, I'd say that computing 10,000 structures using DFT and with no access to a large cluster where to parallelize the computations would take too much time.
Since you're new to chemistry; I'd advise you to stay away from 3D QSAR for the time being. It does provide its advantages, but carries along a large quantity of complexities which can just be skimmed over when working with 2D structures.
EDIT:
I just noticed in the website you link they use models to predict the structure of a molecule as modeled by DFT. In that case you necessarily have to use DFT to obtain the structure. It appears as if they were suggesting to use the 3D structure as input to the model; but I doubt that's the case, they evidently meant that you should train a model on 3D data so that it will later be able to predict other 3D structures from the 2D information.
