If you have an intuition about which results are better than which, you could try to come up with a bunch of examples and order them by goodness and then try to design a loss function that matches these intuitions.  However, my experience is that this kind of methodology doesn’t always yield good results.  For instance, in autoregressive models of audio, the best loss seems to be KL-divergence even though it doesn’t take into account how far off one is.  I have experimented with losses that take into account how far off one is, such as Wasserstein and Cramer distance, but found them to yield worse results than simple KL.  So I guess the best advice is just to experiment, and that seems to be what you already are planning to do