It is also important to consider how the models performed in terms of ranking the bandgaps of the polymorphs. Often in crystal structure prediction, generated structures are ranked according to some desired property, often the lattice energy. Figure shows the rankings of the bandgap for QQQCIG (red), CORONE (orange), and PERLEN (purple) for models trained on the OMDB, OCELOT, and fine-tuned with (a) and without (b) angle featurization in the graph representation. These colors correspond to those in Figure and Figure . The packing motifs of the polymorphs of the three structures mentioned are shown as well. We show a similar plot with all the polymorphs in the PAH101 dataset in the SI. The ground truth ranking is shown in the far left column. As can be seen, the polymorphs of QQQCIG are fairly spread out in terms of their ground truth bandgap. Models trained with the OMDB mainly preserve the ranking with the exception of the lowest bandgap polymorph regardless of the graph representation used. The ranking of the lowest bandgap polymorph actually worsens with OCELOT but then is significantly improved with fine-tuning for models trained with angle featurization. However, the four polymorphs do end up being ranked fairly close together rather than the nice spread we see in the ground truth labels. On the other hand, models trained without angle featurization did not significanly improve the ranking of the lowest bandgap polymorph. Almost opposite behavior is observed for the polymorphs of CORONE: rather than becoming almost degenerate at the final ranking, the gap widens between the OMDB, OCELOT, and fine-tuning for models trained without angle featurization. For those with angle featurization, OMDB does the best at preserving the rank of the polymorphs before a gap emerges when training on OCELOT. Rather than improve this gap, fine-tuning the OMDB-trained model with OCELOT actually makes it worse. Finally, for PERLEN the models similarly preserve the rank of the two polymorphs, regardless of graph representation. However, fine-tuning, regardless of graph representation, does appear to make this gap worse. While bandgap is not a property that is typically ranked, as there is usually a qualitative range a material needs to fall under to be considered an organic semiconductor, it is still of interest to the field of polymorphism and property/structure prediction to know if these models can preserve/achieve accurate rankings. Full ranking plots for all of the polymorphs within the PAH101 dataset are available in Figure in the SI.