We adapted an existing approach to identifying stabilisable crystal structures from prediction sets - the Generalised Convex Hull (GCH) - to improve its application to molecular crystal structures. This was achieved by modifying the Smooth Overlap of Atomic Positions (SOAP) kernel to %reasonably define the similarity of molecular crystal structures \rev{in a more physically motivated way}. The use of the adapted similarity kernel was assessed for several organic molecular crystal landscapes, demonstrating improved interpretability of the resulting machine learned descriptors. We also demonstrate that the adapted kernel results in improved performance in predicting lattice energies using Gaussian process regression. Our overall findings highlight a sensitivity of similarity kernel based landscape analysis methods to kernel construction, which should be considered when applying these methods.