  We have developed an efficient crystal structure prediction (CSP) method for
desired chemical compositions, specifically suited for compounds featuring
recurring molecules or rigid bodies. We applied this method to two metal
chalcogenides: $\text{Li}_3\text{PS}_4$ and
$\text{Na}_6\text{Ge}_2\text{Se}_6$, treating $\text{PS}_4$ as a tetrahedral
rigid body and $\text{Ge}_2\text{Se}_6$ as an ethane-like dimer rigid body.
Initial trials not only identified the experimentally observed structures of
these compounds but also uncovered several novel phases, including a new
stannite-type $\text{Li}_3\text{PS}_4$ structure and a potential metastable
structure for $\text{Na}_6\text{Ge}_2\text{Se}_6$ that exhibits significantly
lower energy than the observed phase, as evaluated by density functional theory
(DFT) calculations. We compared our results with those obtained using USPEX, a
popular CSP package leveraging genetic algorithms. Both methods predicted the
same lowest energy structures in both compounds. However, our method
demonstrated better performance in predicting metastable structures. The method
is implemented with Python code which is available at
https://github.com/ColdSnaap/sgrcsp.git.
