Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which leads to suboptimal alignments, especially when the similarity among the proteins is low. Results: We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure datasets that span various structural folds and represent different protein similarity levels. Availability: A web-based alignment tool, a downloadable executable, and detailed alignment results for the datasets used here are available at http://sacan.biomed.drexel.edu/Smolign and http://bio.cse.ohio-state.edu/Smolign.