The first application we describe is for TTBI, a triptycene derivative with five known polymorphs and a propensity for forming highly porous, hydrogen-bonded organic frameworks . The initial landscape used to train the cNNP was reported by Zhu et. al. and was produced using the FIT+DMA potential, which does an adequate job at identifying the experimental structures and separating them from the bulk of the predicted structures. However, the relative energy differences between the polymorphs are questionable with the gap between the global energy minimum structure (corresponding to the densely-packed ϵ polymorph) and the least dense porous γ polymorph at nearly 100 kJ mol -1 . Although solvent incorporated in the voids was shown to stabilize the porous polymorphs during growth , the FIT+DMA polymorph energy differences seem unreasonably large energy. The relative energies are also sensitive to the calculation method: DFTB3-D3 optimization reduces the energy gap between the polymorphs , as do predictions using the W99+DMA force field . Understanding the achievable energetic range for metastable crystal structures with attractive properties is important for developing CSP for materials discovery. However, calculating higher level energies for such CSP landscapes has thus far been too computationally expensive due to both the large size of the structures and the scale of the landscape: the TTBI CSP landscape used here contains 14997 distinct structures. Furthermore, the landscape exhibits a diverse range of structures covering a very wide density range and from primarily dispersion-bound structures to hydrogen bonded structures: this diversity in intermolecular interactions is a further challenge to training a MLP to predict accurate energies.